Implementation Of Blockchain Within Supply Chain to Offset Logistics Issues Caused by Covid 19

At present, blockchain technology is becoming widespread in logistics, because it can enhance the efficiency of supply chains. This issue became particularly important because of Covid-19 pandemic which has led to serious disruption in goods supply around the world. This paper investigates the feasibility and aspects of blockchain technology implementation. On the basis of received information, a theoretical model was created. A secondary methodology is used in this research. Through this method, previous studies carried out in the same area are analyzed and evaluated to justify the implementation of blockchain within contemporary supply chain systems. It is assessed that a blockchain model can indeed be theoretically implemented with supply chains. However, its implementation can cause various unprecedented issues. In the case of timely resolution of issues, the application of such model can be successful. Proposed theoretical model is sustainable for long term solutions to minor issues that can occur. It can exponentially speed up the material transport and approval processes. It was determined that the model can increase supply chain efficiency by 30%. With such a rate of efficiency ready at the disposal of industrial organizations, they can avoid the potential pitfalls and challenges created by the pandemic's effects on operations and personnel.

Cognitive-behavioral therapy for panic disorders.

This article discusses the treatment of panic disorder in terms of cognitive behavioral psychotherapy. Panic disorder is one of the most common anxiety disorders. The etiology of panic disorder assumes the coexistence of genetic and environmental factors. Panic attacks often accompany other mental and somatic diseases. The constant feeling of intense anxiety with a number of somatic symptoms affects the deterioration of everyday functioning, significantly reducing one's quality of life. The global situation caused by SARS-CoV-2 may correlate with the increase in the incidence of panic disorder. The introduction of appropriate therapeutic interventions at the earliest possible stage of the disease gives patients a chance for long-term remission. So far, it has been proven that the best results of pharmacological treatment are achieved thanks to the antidepressant and benzodiazepine drug groups. One of the non-pharmacological interventions with the highest clinical effectiveness is cognitive-behavioral psychotherapy. Compared to other trends, cognitive- behavioral therapy is characterized by the most reliable research proving its high effectiveness. Psychotherapy of panic disorders in the cognitive-behavioral approach includes a complete conceptualization of the patient's problem, taking into account individual theoretical models. Treatment of panic attacks is based on a protocol which takes into account both cognitive and behavioral interventions, e.g. psychoeducation, cognitive restructuring, breathing training, relaxation exercises and both interoceptive and in vivo exposures. Research results indicate that this therapy is an alternative form of treatment for panic disorders. Sources indicate that the effectiveness of interventions based on cognitive-behavioral therapy is comparable to that of pharmacotherapy.Copyright © 2022 Termedia Publishing House Ltd.. All rights reserved.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Stress, relational turbulence, and communal coping during the COVID-19 pandemic

This study examined how increased stress during the COVID-19 pandemic contributed to relational turbulence and undermined dyadic coping. Using longitudinal data, this study also explored how enacting communal coping mitigates stress and conditions of relational turbulence over time. A sample of 151 U.S. dyads (302 individuals) completed online surveys about their relationship once per week for four weeks during the early stages of the COVID-19 pandemic. Data were analyzed using multilevel modeling. Consistent with hypotheses, stress was positively associated with the relationship conditions that give rise to relational turbulence and heightened relational turbulence was negatively associated with communal coping. Longitudinal analyses revealed that communal coping enacted in one week was associated with decreased stress and improved relationship quality in subsequent weeks. The findings are discussed in terms of their practical implications and contributions to theory.

Theoretical Model for the Analysis of Community-Based Tourism: Contribution to Sustainable Development

This work aims to show a theoretical model of community-based tourism, to explain its component subsystems, to provide its theoretical–methodological foundation and to discuss the indications of its practical instrumentation in facing the changes that tourism of the future imposes and will impose. The research was carried out in the tourist context of Ecuador, for which the deductive method was applied, which allowed for examining the problem, and the more general theories related to tourist activity, which allowed for identifying the premises and objectives of the work to reach accurate conclusions on the subject studied. This was a mixed investigation that allowed for integrating the contributions of qualitative and quantitative analyses in the treatment and processing of information. The results included achieving systematization of the theoretical models linked to community-based tourism and, from a practical point of view, obtaining a new model of community-based tourism, a graphic representation of the subsystems that form this model, and its arguments. The findings show the need to update the community-based tourism model as a contribution to the scientific development of tourism as well as the systemic nature of its components from a new perspective of analysis that considers the need for changes as a developmental factor.

Monitoring COVID-19 in Colombia during the first year of the pandemic.

Background: COVID-19 cases in Medellín, the second largest city in Colombia, were monitored during the first year of the pandemic using both mathematical models based on transmission theory and surveillance from each observed epidemic phase. Design and Methods: Expected cases were estimated using mandatory reporting data from Colombia's national epidemiological surveillance system from March 7, 2020 to March 7, 2021. Initially, the range of daily expected cases was estimated using a Borel-Tanner stochastic model and a deterministic Susceptible-Infected-Removed (SIR) model. A subsequent expanded version of the SIR model was used to include asymptomatic cases, severe cases and deaths. The moving average, standard deviation, and goodness of fit of estimated cases relative to confirmed reported cases were assessed, and local transmission in Medellin was contrasted with national transmission in Colombia. Results: The initial phase was characterized by imported case detection and the later phase by community transmission and increases in case magnitude and severity. In the initial phase, a maximum range of expected cases was obtained based on the stochastic model, which even accounted for the reduction of new imported cases following the closure of international airports. The deterministic estimate achieved an adequate fit with respect to accumulated cases until the conclusion of the mandatory national quarantine and gradual reopening, when reported cases increased. The estimated new cases were reasonably fit with the maximum reported incidence. Conclusion: Adequate model fit was obtained with the reported data. This experience of monitoring epidemic trajectory can be extended using models adapted to local conditions.

Theoretical model of investigating determinants for a successful Electronic Assessment System (EAS) in higher education.

Electronic assessment (e-assessment) is an essential part of higher education, not only used to manage a large class size of students' learning performance and particularly in assessing the learning outcomes of students. The e-assessment data generated can not only be used to determine students' study weaknesses to develop strategies for teaching and learning, but also in the development of essential teaching and learning pedagogies for online teaching and learning. Despite the wider adoption of Information and Communication Technology (ICT) technologies due to the COVID-19 pandemic, universities still encountered numerous problems during the transformation to electronic teaching as most educators struggled with the effective implementation of the Electronic Assessment System (EAS). The successful launch of EAS relied heavily on students' use intention towards the new and unfamiliar electronic system, which was actually unknown to the project managers of EAS. It is therefore important to understand students' views and concerns on EAS and the proactive measures taken by universities to enhance students' acceptance and intention of usage. Although most studies investigate students' acceptance of online learning, there is still little research on the adoption of e-assessment. In this regard, we propose to develop a theoretical model based on students' perceptions of EAS. Based on the Technology Acceptance Model (TAM) and a major successor of TAM, an electronic assessment system acceptance model (EASA model) is developed with key measures including system adoption anxiety, e-assessment facilitation, risk reduction amid, etc. The data is obtained through a survey among current students at a local university, and structural equation modeling (SEM) is applied to analyze the quantitative data. This study has a significant impact on improving educators' use of e-assessment in order to develop essential online teaching and learning pedagogy in the future.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses.

BACKGROUND: During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. OBJECTIVE: We propose an elaboration likelihood model-based theoretical model to understand the persuasion process of COVID-19-related misinformation on social media. METHODS: The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19-related misinformation feature includes five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic-related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. RESULTS: Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. CONCLUSIONS: Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

Health conditions of people on the streets during the COVID-19 pandemic in light of Nightingale’s Environmental Theory

Objective: The objective was to analyze the health conditions of people living on the streets, in the COVID-19 scenario, based on the contributions of the Environ-mental Theory. Methods: This is a theoretical-reflexive analysis. Results: Two cate-gories were developed: The dichotomy between living on the street and the Night-ingalian assumptions and;The reality of people living on the street in times of pan-demic by COVID-19. The first category addressed the difference between what is experienced by people on the street and what the Nightingalian assumptions say about health conditions. The second category showed the insertion of these people in the context of prevention measures. Final considerations: It is of fundamental importance to discuss the access to prevention and health promotion strategies by these people and to promote specific actions for the reality in which they live.

Social responsibility perspective in public response to the COVID-19 pandemic: a grounded theory approach.

BACKGROUND: Combating viral outbreaks extends beyond biomedical and clinical approaches; thus, public health prevention measures are equally important. Public engagement in preventive efforts can be viewed as the social responsibility of individuals in controlling an infectious disease and are subjected to change due to human behaviour. Understanding individuals' perception of social responsibility is crucial and is not yet explored extensively in the academic literature. We adopted the grounded theory method to develop an explanatory substantive theory to illustrate the process of how individual responded to the outbreak from a social responsibility perspective. METHODS: In-depth interviews were conducted among 23 Malaysians either through telephone or face-to-face depending on the participant's preference. Both purposive and theoretical sampling were used. Participants were invited to share their understanding, perceptions and activities during the COVID-19 pandemic. They were further probed about their perceptions on complying with the public health interventions imposed by the authorities. The interviews were audio-recorded and transcribed verbatim. Data was analysed via open coding, focus coding and theoretical coding, facilitated by memoing, sketching and modelling. RESULTS: Study findings showed that, social responsibility is perceived within its role, the perceived societal role responsibility. In a particular context, an individual assumed only one of the many expected social roles with their perceived circle of responsibility. Individuals negotiated their actions from this perspective, after considering the perceived risk during the outbreak. The four types of behaviour depicted in the matrix diagram facilitate the understanding of the abstract concept of negotiation in the human decision-making process, and provide the spectrum of different behaviour in relation to public response to the COVID-19 pandemic. CONCLUSIONS: Our study adopted the grounded theory approach to develop a theoretical model that illustrates how individual response to COVID-19 preventive measures is determined by the negotiation between perceived societal role responsibility and perceived infection risk. This substantive theoretical model is abstract, thus has relevance for adoption within similar context of an outbreak.

Study on the influencing factors of health information sharing behavior of the elderly under the background of normalization of pandemic situation

This study aims to solve the problem of unwise judgment, decisions, and correspondingly dangerous behaviors caused by error health information to the elderly. Based on the MOA model and self-determination theory, this paper constructs a health information sharing model for the elderly and analyzes it with Amos's structural equation model. The study finds that media richness, health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic positively influence health information sharing behavior. In contrast, perceived risks have a significant negative impact on health information sharing behavior. At the same time, media richness positively affects health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic but has no significant impact on perceived risks. Health literacy positively affects perceived benefits but does not significantly affect the perceived risks and negative emotions of the coronavirus epidemic. This study aims to assist government and online social platforms in taking relevant measures under the background of normalization of the pandemic situation, controlling the spread of error health information among the elderly, and guiding the elderly to share health information better. © 2021 International Consortium for Electronic Business. All rights reserved.

COVID-19 virus behavior simulation model: Case of study Bogota, Colombia

Objetive The purpose of this article is to present a prediction on the epidemiological behavior of the COVID-19 virus for the city of Bogota, Colombia and determine the possible incidence of the different restriction measures. Material and Methods: a dynamic model based on state information was built for all the analysis variables in the framework of a SEIR model. Results and Conclusions: it was determined that the incidence of effective isolation measures significantly affects the behavior of the disease. Results are presented for two different scenarios, with their respective sensitivity analysis using the Monte Carlo method. It is concluded that it is necessary to increase testing and reduce the time needed to get the results. It is also crucial to generate and deploy social policies that make the fulfilment of an effective quarantine possible. Finally, the restrictions of the model for decision-making in terms of public policy are shown.

Paediatric nurses' burnout, quality of life and perceived patient adverse events during the COVID-19 pandemic: Testing an integrated model using structural equation modelling.

PURPOSE: A model was hypothesised by integrating two theoretical models: the compassion satisfaction-compassion fatigue and empowerment models. This study aimed to assess the extent to which this integrated model can explain the relationships between paediatric nurses' burnout quality of life, perceived patient safety and work-related variables during the COVID-19 pandemic. BACKGROUND: Nurses' burnout is negatively associated with quality of life (QOL) and positively with patient safety. Several theoretical models were introduced to explain burnout determinants and outcomes such as Golembiewski, Munzenrider and Stevenson model, Leiter and Maslach's process model, and Lee and Ashforth's model. However, few models described burnout in relation to QOL or patient safety. METHODS: A sample of 225 paediatric nurses responded to questionnaires about burnout, QOL, adverse events and work-related variables. Compassion satisfaction - compassion fatigue and empowerment models were integrated into a single model and tested using structural equation modelling analysis. This study was prepared and is reported according to the STROBE checklist. RESULTS: The final model explained 65% of the variance of burnout and 37% of the variance of QOL. The work-related variables (co-workers' support, job satisfaction, satisfaction with the monthly salary, participation in continuous education and exposure to violence) are predicting paediatric nurses' burnout and quality of life. CONCLUSION: The Compassion satisfaction - compassion fatigue -Empowerment integrated model allows for assessing the different paths in the relationship between work-related variables and burnout. RELEVANCE TO CLINICAL PRACTICE: These results might be essential for nursing managers to develop strategies that improve nurses' work environment and minimise their burnout during COVID-19 pandemic. These strategies should focus on enhancing co-workers' support, job satisfaction and participation in continuous education. Furthermore, paediatric nurses should be protected from any violence.

Government Epidemic Prevention and Economic Growth Path Under Public Health Emergency: Theoretical Model and Simulation Analysis.

This paper constructs a partial equilibrium model under public health emergency shocks based on economic growth theory, and investigates the relationship between government intervention and virus transmission and economic growth path. We found that both close contacts tracing measures and isolation measures are beneficial to human capital stock and economic output per capita, and the effect of close contact tracing measures is better than that of isolation measures. For infectious diseases of different intensities, economic growth pathways differed across interventions. For low contagious public health emergencies, the focus should be on the coordination of isolation and tracing measures. For highly contagious public health emergencies, strict isolation, and tracing measures have limited effect in repairing the negative economic impact of the outbreak. The theoretical model provides a basic paradigm for the future researches to study economic growth under health emergencies, with good scalability and robustness.

Theory-Informed Models: Application to Nursing Practice.

The processes for review and confirmation of a theoretical model, its translation into current clinical practice, and the evaluation of outcomes will be presented. The authors' experience at the Seattle Cancer Care Alliance in Washington illustrates the value and relevance of theoretical models in oncology care.

Age-structured SIR model and resource growth dynamics: a COVID-19 study.

In this paper, we discuss three different response strategies to a disease outbreak and their economic implications in an age-structured population. We have utilized the classical age structured SIR-model, thus assuming that recovered people will not be infected again. Available resource dynamics is governed by the well-known logistic growth model, in which the reproduction coefficient depends on the disease outbreak spreading dynamics. We further investigate the feedback interaction of the disease spread dynamics and resource growth dynamics with the premise that the quality of treatment depends on the current economic situation. The very inclusion of mortality rates and economic considerations in the same model may be incongruous under certain positions, but in this model, we take a "realpolitik" approach by exploring all of these factors together as it is done in reality.

COVID-19 Outbreak and Physical Activity in the Italian Population: A Cross-Sectional Analysis of the Underlying Psychosocial Mechanisms.

Italy is one of the first European epicenters of the COVID-19 pandemic. In attempts to hinder the spread of the novel coronavirus disease, Italian government hardened protective measures, from quarantine to lockdown, impacting millions of lives dramatically. Amongst the enacted restrictions, all non-essential activities were prohibited as well as all outdoor activities banned. However, at the first spur of the outbreak, for about a dozen of days, physical and sports activities were permitted, while maintaining social distancing. In this timeframe, by administering measures coming from self-determination theory and theory of planned behavior and anxiety state, in an integrated approach, we investigated the prevalence of these activities by testing, via a Structural Equation Model, the influence of such psychosocial variables on the intention to preserve physical fitness during the healthcare emergency. Through an adequate fit of the hypothesized model and a multi-group analysis, we compared the most COVID-19 hit Italian region - Lombardy - to the rest of Italy, finding that anxiety was significantly higher in the Lombardy region than the rest of the country. In addition, anxiety negatively influenced the intention to do physical activity. Giving the potential deleterious effects of physical inactivity due to personal restrictions, these data may increase preparedness of public health measures and attractiveness of recommendations, including on the beneficial effects of exercise, under circumstances of social distancing to control an outbreak of a novel infectious disease.